BIFIE.mva | R Documentation |
Conducts a missing value analysis.
BIFIE.mva( BIFIEobj, missvars, covariates=NULL, se=TRUE ) ## S3 method for class 'BIFIE.mva' summary(object,digits=4,...)
BIFIEobj |
Object of class |
missvars |
Vector of variables for which missing value statistics should be computed |
covariates |
Vector of variables which work as covariates |
se |
Optional logical indicating whether statistical inference based on replication should be employed. |
object |
Object of class |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
A list with following entries
stat.mva |
Data frame with missing value statistics |
res_list |
List with extensive output split
according to each variable in |
... |
More values |
############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object BIFIEdata <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) # missing value analysis for "scsci" and "books" and three covariates res1 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books" ), covariates=c("ASMMAT", "female", "ASSSCI") ) summary(res1) # missing value analysis without statistical inference and without covariates res2 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books"), se=FALSE) summary(res2)
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